The effect of different views on segmentation or object detection in natural scenes has not been thoroughly studied, as feature characteristics are relatively preserved even under rotation of the object in that context. Nonetheless, preliminary studies of classification model performance on video frames suggests that minimal pixel-level changes can impact performance [1]}. By contrast, substantial occlusion and distortion occurs in off-nadir overhead imagery, complicating segmentation and placement of geospatially accurate object footprints, as shown in Figure REF A-B. Furthermore, due to the comparatively small size of target objects (e.g. buildings) in overhead imagery, changing view substantially alters their appearance (Figure REF C-D). We expect similar challenges to occur when detecting objects in natural scene images at a distance or in crowded views. Existing solutions to occlusion are often domain specific [2]} or rely on attention mechanisms to identify common elements [3]} or landmarks [4]}. The heterogeneity in building appearance in overhead imagery, and the absence of landmark features to identify them, makes their detection an ideal research task for developing domain-agnostic models that are robust to occlusion.
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